Internet retail commerce enables retailers operating web sites to collect extensive data about customers' web browsing and shopping activities. This data may be associated with a particular customer who has opted to identify him or herself to the retailer by registering, or may be associated with a unique device identifier such as a “cookie” assigned by the retailer to the particular user's computer or other communication device. In such cases, a pattern of activity may be associated with the cookie, although the identity of the customer is not known. Other commerce and communications activities may generate extensive data that may be stored for analysis.
Data collected by these means is analyzed predict the future commercial activities of the customers. Customers may be evaluated, and assigned a “lifetime value” based on the data. Customers with higher value may be selected to receive certain promotions and discounts, and may be studied to determine whether they have common characteristics. Other potential customers with such characteristics might also be targeted with promotions, with the intent that they would be more likely than most to become a high value customer.
Marketing analysts generally evaluate customers in three primary characteristics: recency, monetary, and advocacy. High value customers are those who have a high score in each of these areas, having visited or made purchases recently, having made relatively high cost or high profit purchases, and having a pattern of recommending the retailer to other potential customers.
Existing marketing analysts use the extensive and detailed data collected during web browsing and electronic transactions to generate estimates of customer values. This requires the collection and extended storage of immense amounts of data, and significant resources to analyze the data. Typically, custom-created software applications are required for each retailer, which consumes significant time and resources. Moreover, even well-crafted analyses of extensive data have significant limitations.
Existing techniques require that the analyst develop a hypothesis about the characteristics of customers before conducting an “experiment” in the form of data analysis. Such an experiment may compare various groups with different demographic, or other commercial activity characteristics, in order to determine which is more likely to be a highly valued customer in the future. However, there may be important patterns in the data that the experimenter never discovers, due to testing the wrong hypotheses.
Another disadvantage of conventional customer data analysis techniques is that they do not account for the changing data, and often view only a snapshot at a particular time, or compare multiple snapshots at limited time intervals. This has limited effect to determine changing qualities of different customers, such as those who have increasing or decreasing value.
The present invention overcomes the limitations of the prior art by providing a method and facility for displaying information about a multitude of different customers. The method includes, for each customer, receiving a set of numeric values, each associated with one of a plurality of different parameters. A symbol is generated for each customer, and the symbols are spatially arranged, based on the numeric values. The method may include collecting customer interactions during web browsing, and converting interaction data to a limited data set of the values for display and storage. The values may be displayed in three dimensional format for viewing, and may be revised over time and as additional customer interactions occur.